New research from Anthropic explores how AI use may affect critical thinking
Artificial Intelligence is moving faster than anyone predicted. Every week brings another announcement, another model, another story about how productivity has doubled somewhere. It is tempting to believe we have finally found a lever that removes friction from thinking itself. A recent research paper from Anthropic suggests we should think twice before believing that critical thinking in the age of AI is becoming less important. If f anything the relationship between AI and critical thinking is becoming more important than ever (note that it’s not an either/or situation!)
AI and critical thinking – does faster mean better?
In a controlled experiment developers completed identical coding tasks using a library none of them had worked with before. Half had access to an AI assistant, and the other half worked without one. Once the task was done, both groups were tested on conceptual understanding, code reading, and debugging. No AI usage was allowed during the test.
The results were striking. The developers who relied most heavily on AI scored lower across the board, with the biggest drop showing up in debugging.
The group without AI also made more mistakes along the way and took a little longer to complete the tasks. But those mistakes turned out to be the entire point: Struggling with errors forced them to actually understand what they were building. AI made the task feel easier, but it did not produce a meaningful speed advantage overall. The most important takeaway is that the people who relied on it heavily understood less when the work was over. This is indeed an important reminder that AI and critical thinking should work together rather than compete with one another.
How people balance AI and critical thinking is a key differentiator
The researchers identified six patterns of AI interaction. At one extreme, “pure delegation”: ask the model, paste the answer, move on. That group scored in the thirties.
At the other extreme, a pattern they called “generation then comprehension”: the developer let AI produce something, then interrogated it, questioned it, and worked to understand why it functioned. That group scored in the eighties.
The difference had nothing to do with access to technology. It had everything to do with whether the human stayed in the driver’s seat, and used their own judgment and critical thinking skills. The strongest performers treated AI and critical thinking as complementary rather than allowing one to replace the other.
I have been in consulting long enough to recognize this pattern immediately. Right now, a lot of leaders are trying to figure out what to do with AI. They want to give their teams guidance on when to use it, how to use it, and how to make sure it does not become the crutch that eventually leads to their people forgetting how to walk. That is not a hypothetical concern. The Anthropic study shows it happening in real time, in a controlled setting, with experienced professionals.
Critical Thinking in the Age of AI Still Requires Discipline
This is not a new problem. When I read this study, I was struck by how familiar it felt. The terminology might be new, but the principle is not. For more than six decades, Kepner Tregoe has operated on a simple conviction: rational thinking is a discipline, not an instinct. It has to be practiced deliberately and applied consistently. We build capability through cognitive effort, especially in ambiguity and error. If AI removes that effort, skill formation erodes. If AI amplifies structured reasoning, skill formation can accelerate. The difference is not the tool; it is the process discipline around the tool. That is exactly why critical thinking in the age of AI deserves as much attention as AI itself.
In fact, the study’s findings line up naturally with KT’s frameworks. Situation Appraisal requires us to separate and clarify all concerns before acting. Many organizations fixate on one question: does AI accelerate task completion? Two other critical questions which people often regard as secondary: does AI inhibit skill formation, and are people retaining the capability to supervise AI output responsibly? If leaders focus mainly on speed, they may improve throughput while weakening the judgment and diagnostic skills the business depends on. If we let AI drive the process instead of support it, the diagnostic skills our operations depend on will slowly weaken.
Where AI and critical thinking diverge
The debugging findings are especially worth reflecting on. The largest gap between the two groups showed up in diagnosing and correcting errors. “Debugging” is essentially structured reasoning under uncertainty, and as any programmer knows, it is where deduction and methodical problem solving is essential. It is one practical expression of the concepts found in KT Problem Analysis: Distinguishing what is happening from what is not, examining deviations across what, where, when, and extent.
That discipline is built through repetition. When AI resolves errors instantly, the repetition disappears. The LLM produces an answer, the human accepts it, and their thinking muscle weakens.
Before deciding how to integrate AI into workflows, leaders need a structured approach to AI and critical thinking, and should ideally conduct a disciplined KT Decision Analysis. There are objectives that must be met, tradeoffs that must be understood, and risks that must be anticipated.
KT’s Potential Problem Analysis asks not only what could fail, but why it would fail and how you would detect early warning signs. The gradual erosion of diagnostic expertise through unreflective AI adoption is exactly the kind of slow-moving risk that never triggers an alarm until it is too late.
What high performers can teach us about AI and critical thinking
Of course there is an opportunity here too. The highest-performing participants in the study did not avoid AI. They used it as a thinking partner, generating output and then examining it, questioning it, testing assumptions. They stayed mentally engaged and kept doing the thinking. That is what Potential Opportunity Analysis cultivates: disciplined human judgment amplified rather than replaced.
he organizations that will thrive in an AI-enabled economy will not necessarily be the ones that adopt new tools the fastest. They will be the ones that understand the relationship between AI and critical thinking, embedding AI tools within a culture of disciplined thinking. This is essentially a culture that insists that AI accelerates work without replacing human judgment, and that protects the cognitive capabilities that good supervision requires.
The question worth asking is whether your organization has a structured framework for how to adopt AI, or whether it is simply happening. For over six decades, Kepner Tregoe has helped organizations think with rigor, particularly under pressure. The arrival of AI does not make that mission obsolete. It makes critical thinking in the age of AI more important than ever.
If this topic matters to you as a leader, the Anthropic paper, How AI Impacts Skill Formation, published on 3 February 2026, is well worth your time.
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